Amy Maret

Recent Posts

Buying a plane ticket can be a gamble. Right now, it might be a good price, but who’s to say it won’t drop in a day—a week? Not only that, it may be cheaper to take that Sunday night flight instead of Monday morning. And oh—should you fly into Long Beach or LAX? As a frequent traveler (for leisure and work!) and deal seeker, I face dilemmas like these a lot.

The good news is that there are loads of apps and websites to help passengers make informed travel decisions. But how? How can an app—say, Hopper—know exactly when a ticket price will hit its lowest point? Is it magic? Is there a psychic in the backroom predicting airline prices with her crystal ball?

Not quite.

While it seems like magic (especially when you do land that great deal), forecasting flight prices all comes down to predictive analytics—identifying patterns and trends in a vast amount of data. And for the travel industry in particular, there’s incredible opportunity to use data in this way. So, let’s put away the crystal ball (it won’t fit in your carry on) and look at how travel companies and data scientists are using the tremendous amount of travel data to make predictions like when airfare will hit its lowest point.

In order to predict what will happen in the future (in this case, how airfare may rise and fall), you need a lot of data on past behaviors. According to the Federal Aviation Administration (FAA), there are nearly 24,000 commercial flights carrying over two million passengers around the world every day. And for every single one of those travelers, there’s a record of when they purchased their ticket, how much they paid, what airline they’re flying, where they’re flying to/from, and when they’re traveling. That’s a ton of data to work with!

As a researcher, I get excited about the endless potential for how that amount of historical data can be used. And I’m not the only one. Companies like Kayak, Hopper, Skyscanner, and Hipmunk are finding ways to harness travel data to empower consumers to make informed travel decisions. To quote Hopper’s website: their data scientists have compiled data on trillions of flight prices over the years to help them make “insightful predictions that consistently perform with 95% accuracy”.

While the details of Hopper are intentionally vague, we can assume that their team is using data mining and predictive analytics techniques to identify patterns in flights prices. Then, based on what they’ve learned from these patterns, they build algorithms that let customers know when the best time to purchase a ticket is—whether they should buy now or wait as prices continue to drop leading up to their travel date. They may not even realize it, but in a way those customers are making data-driven decisions, just like the ones we help our clients make every day.

As a Market Researcher, I’m all about leveraging data to make people’s lives easier. The travel industry’s use of predictive modeling is mutually beneficial—consumers find great deals while airlines enjoy steady sales. My inner globetrotter is constantly looking for ways to travel more often and more affordably, so as I continue to discover new tools that utilize the power of data analytics to find me the best deals, I’m realizing I might need some more vacation days to fit it all in!

So the next time you’re stressed out about booking your next vacation, just remember: sit back, relax, and enjoy the analytics.

Amy M. is a Project Manager at CMB who will continue to channel her inner predictive analyst to plan her next adventure.

“Which Starbucks Drink Are You?” “What Role Would You Play in a Disney Movie?” “Which ‘Friends’ Character Are You Least Like?” These are the deep existential questions posed on websites like BuzzFeedand PlayBuzz. My Facebook and Twitter feeds are continuously flooded by friends posting their quiz results, and the market researcher in me can’t help compare them to the segmentationwork that we do at CMB every day.

So let’s take a closer look at a few of the basic concepts segmentations share with Buzzfeed quizzes and learn why I’m not too worried about losing my job to BuzzFeed writers just yet:

You answer a predetermined set of questions. In the Starbucks drink quiz, you might be asked to identify your favorite color or your ideal vacation spot, even though these questions have nothing to do with Starbucks. At CMB, we focus on the product or service category at hand, we make sure we include questions that measure real customer needs. That way, we know our final solution will have implications in driving customer behavior. It’s much easier to see the relevance of a solution when the questions we ask have face validity.

You are assigned to a group based on your answers. While I don’t know exactly what happens on the back end of a BuzzFeed quiz, there must be some basic algorithm that determines whether you are a Double Chocolaty Chip Frappuccino or Very Berry Hibiscus Refresher. However, as far as I know, the rules behind this algorithm are entirely made up by the author of the quiz, probably based on hours hanging out at their local Starbucks. When we conduct a market segmentation study, we typically use a nationally representative sample, which allows our clients to see how large the segments are and what true opportunities exist in the market. We also ensure that we end up with a set of clearly distinct segments that are both statistically solid and useful so that our clients can feel confident implementing the results.

Each group is associated with certain traits. When your quiz results pop up, they usually come with a brief explanation of what the results mean. If you are an Iced Caramel Macchiato, for example, you're successful, honest, and confident. But, if you are a Passion Iced Tea, you are charismatic and hilarious. As a standard part of our segmentation studies, CMB delivers an in-depth look at key measures for each segment, such as demographics, brand preference, and usage, to demonstrate what makes them unique, and how they can be reached. We tailor these profiles to meet the needs of the client, so that they can be used to solve real business problems. For example, the sales team could use these segmentation results to personalize each pitch to a particular type of prospect, the creative team could target advertisements to key customer groups, or finance managers could ensure that budgets are being directed towards those with whom they will be most effective.

I’ll be the first person to admit that personality quizzes are a great way to waste some free time and maybe even learn something new about yourself. But what’s really fun is taking the same basic principles and using them to help real businesses make better decisions. After all, a segmentation is only useful when it is used, and that is why we make our segmentation solutions dynamic, living things to be reapplied and refreshed as often as needed to keep them actionable.

Amy Maret is a Project Manager at CMB with a slight addiction to personality quizzes. In case you were curious, she is an Espresso Macchiato, would play a Princess in a Disney movie, and is least like Ross from Friends.

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What if you could hop off a seven hour flight from New York to London feeling refreshed and entirely jet lag-free? That’s the question British Airways has been trying to answer for years. From enhanced entertainment and meal offerings to carefully-designed lighting and noise reduction measures, many of the recent updates to British Airways’ fleet have been centered on creating the perfect customer experience in a notoriously tricky industry.Their latest innovation is “The Happiness Blanket.” The blanket is embedded with LEDs and connected via Bluetooth to a headband containing sensors that read electrical fluctuations in the wearer’s neurons. According to the promotional video released by the airline, if brain activity indicates that the wearer is calm and relaxed, the surface of the blanket turns blue. If the wearer is stressed or anxious, the blanket turns red.

As a researcher, this opportunity for seemingly effortless, real-time data collection piqued my interest immediately. I see a lot of potential in the ability to capture passengers’ emotional responses to various aspects of the flight experience as they actually experience it. If they had access to accurate emotional response information, flight attendants could find ways to tailor services to accommodate the needs of passengers on an individual level, and data collected across countless flights could provide useful information about what the airline is doing right overall and where they need to improve. With a bit of additional demographic and psychographic information on each passenger, the airline could create marketing campaigns and promotions around the specific experiences and emotional reactions of different subgroups.

At CMB, we know just how much emotions matter. We repeatedly find that the emotional impression left on a customer after an interaction with a brand is a major driver of customer satisfaction, likelihood to recommend, and even future purchase intent across all types of industries. British Airways, by focusing on helping passengers step off its planes feeling satisfied, is creating a subconscious connection between its passengers’ positive emotions and its brand. You can bet that the next time I need to book a flight, I would first look to the airline that got me to Europe feeling refreshed and relaxed, rather than the one that left me dehydrated and drowsy.

However, The Happiness Blanket certainly has its drawbacks as a research tool. Based on the information provided about the blanket so far, it seems that there is no way to tell—on a more detailed level—what emotions the passengers are experiencing, which would have serious consequences. The blanket supposedly turns red when the wearer is anxious or stressed and blue when he/she is calm or relaxed, but there are so many more emotions on the spectrum that are not acknowledged by this system. For example, if two people’s blankets show red, one may be because a passenger is feeling unsafe and afraid on the flight, while the other may be because a passenger is enjoying the adrenaline rush of watching an action movie. If you were to ask those two passengers how they felt after their flights, and whether they would choose to fly with the airline again, you would get two drastically different answers. If British Airways intends to use this data to make real, impactful changes to its service, they will need to find a way to capture nuances like this or they could misinterpret the data entirely and make poor business decisions as a result.

This example provides a basic illustration of why we find that self-reporting is the most accurate way to collect data on something as subjective as emotion. While biometric solutions can sometimes provide a basic emotional read, self-reporting provides a more dependable, and much less expensive, way to get at the discrete emotion being experienced. The only way for the flight attendant to tell the difference between two red blankets would be to ask the passengers how they are feeling. Only then could they properly tailor the service to each person’s experience.

When I told my colleagues about The Happiness Blanket, they kept asking the same questions: how long can the novelty of the blanket sustain its use? Couldn’t it be a bit awkward to have your emotions broadcast to the entire cabin, especially in a situation as sensitive for many people as flying? Maybe it would make more sense to get rid of the blanket aspect entirely and just send the data directly to a computer. That way, the flight attendants could still monitor the data for in-flight use, and it could still be captured for future analysis, but passengers wouldn’t be disturbed by the constant color changes on their (or fellow passengers’) blankets. However, getting passengers to agree to have their brainwaves monitored by an airline could prove a challenge, and with the inaccuracies of this method of data collection, it may not even be worth the investment. Although the idea of being able to read passengers’ emotions directly appeals to me as a researcher, self-reporting is still the only way to capture reliable data on the subjective emotions of customers.

So, is The Happiness Blanket just a clever publicity stunt designed to promote recent enhancements to British Airways’ First and Business Class cabins, or is it a sign of true dedication to research and customer feedback? So far, it seems like the company has primarily been using The Happiness Blanket to attract attention, get consumers engaged with the brand, and show why the company thinks its flights are better than its competitors’ flights. If British Airways is truly trying to capture useable information on their passengers’ reactions to its service through The Happiness Blanket. . .they’ll also need to ask them.

Amy is Senior Associate Researcher at CMB and an avid traveler. She is a bit disappointed that she won’t have the chance to try out the Happiness Blanket on her next trip to Europe.

Understanding the emotional payoffs consumers want and expect is critical to helping brands build and maintain a loyal customer base. Watch our recent webinar to hear Dr. Erica Carranza and Brant Cruz share how we capture these emotional payoffs to inform a range of business challenges, including marketing, customer experience, customer loyalty, and product development.

As a recent graduate, and entrant into the world of professional market research, I have some words of wisdom for college seniors looking for a career in the industry. You may think your professors prepared you for the “real world” of market research, but there are some things you didn’t learn in your Marketing Research class. So what’s the major difference between research at the undergrad level and the work of a market researcher? In the real world, context matters, and there are real consequences to our research. One example of this is how we approach testing for statistical significance.Starting in my freshman year of college, I was taught to abide by a concept that I came to think of as the “Golden Rule of Research.” According to this rule, if you can’t be 95% or 90% confident that a difference is statistically significant, you should consider it essentially meaningless.

Entering the world of Market Research, I quickly found that this rule doesn’t always hold when the research is meant to help users make real business decisions. Although significance testing can be a helpful tool in interpreting results, ignoring a substantial difference simply because it does not cross the thin line into statistical significance can be a real mistake.

Imagine a manager gets the results of a concept test in which a new ad outperforms the old by a score of 54% to 47%; sig testing shows our manager can be 84% confident the new ad will do better than the old ad. The problem in the market research industry is that we typically assess significance at the 95% or 90% level, if the difference between scores doesn’t pass this strict threshold, then it is often assumed no difference exists.

However, in this case, we can be very sure that the new ad is not worse than the old (there’s only a 1% chance that the new ad’s score is below the old). So, the manager has an 84% chance of improving her advertising and a 1% chance of hurting it if she changes to the new creative—pretty good odds. The worst scenario is that the new creative will perform the same as the old. So, in this case, there is real upside in going with the new creative and little downside (save the production expense). But if the manager relied on industry-standard significance testing, she would likely have dismissed the creative immediately.

At CMB, it doesn’t take long to get the sense that there is something much bigger going on here than just number crunching. Creating useable, meaningful research and telling a cohesive story require more than just an understanding of the numbers themselves; it takes creativity and a solid grasp on our clients’ businesses and their needs. As much as I love working with the data, the most satisfying part of my job is seeing how our research and recommendations support real decisions that our clients make every day, and that’s not something I ever could have learned in school.

Amy is a recent graduate from Boston College, where she realized that she had a much greater interest in statistics than the average student. She is 95% confident that this is a meaningful difference.

Join CMB' Amy Modini on February 20th, at 12:30 pm ET, to learn how we use discrete choice to better position your brand in a complex changing market. Register here.